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Visual Saliency is the capability of vision system to select distinctiveparts of scene and reduce the amount of visual data that need to be processed.The presentpaper introduces (1) a novel approach to detect salient regions byconsidering color and luminance based saliency scores using Dynamic ModeDecomposition (DMD), (2) a new interpretation to use DMD approach in staticimage processing. This approach integrates two data analysis methods: (1)Fourier Transform, (2) Principle Component Analysis.The key idea of our work isto create a color based saliency map. This is based on the observationthatsalient part of an image usually have distinct colors compared to theremaining portion of the image. We have exploited the power of different colorspaces to model the complex and nonlinear behavior of human visual system togenerate a color based saliency map. To further improve the effect of finalsaliency map, weutilized luminance information exploiting the fact that humaneye is more sensitive towards brightness than color.The experimental resultsshows that our method based on DMD theory is effective in comparison withprevious state-of-art saliency estimation approaches. The approach presented inthis paperis evaluated using ROC curve, F-measure rate, Precision-Recall rate,AUC score etc.

Salient Region Detection and Segmentation in Images using Dynamic Mode Decomposition